Preface

In the internet age, where an increasing volume of text data is being generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. This book will help you build deep learning models for Natural Language Processing (NLP) tasks that will help you extract valuable insights from text.

We will start by understanding how to install PyTorch and using CUDA to accelerate the processing speed. You'll then explore how the NLP architecture works through practical examples. Later chapters will guide you through important principles, such as word embeddings, CBOW, and tokenization in PyTorch. You'll then learn some techniques for processing textual data and how deep learning can be used for NLP tasks. Next, we will demonstrate how to implement deep learning and neural network architectures to build models that allow you to classify and translate text and perform sentiment analysis. Finally, you will learn how to build advanced NLP models, such as conversational chatbots.

By the end of this book, you'll understand how different NLP problems can be solved using deep learning with PyTorch, as well as how to build models to solve them.